KRLS
is a statistical software package for R
that implements Kernel
Regularized Least Squares (KRLS) as described in Hainmueller
and Hazlett (2013).
KRLS
is a machine learning method that is designed
to flexibly fit response surfaces of the form
y=f(x) as they arise in regression and
classification problems without relying on
linearity, additivity, or other assumptions
that use the columns of the predictor matrix
X directly as basis functions.

The Conjoint Survey Design Tool
assists researchers in creating
multi-dimensional choice experiments that
can be readily incorporated into any
pre-existing web survey software (such as
Qualtrics).
Conjoint analysis is a type of survey
experiment often used by market
researchers to measure consumer
preferences over a variety of product
attributes. Hainmueller,
Hopkins and Yamamoto (2013)
demonstrate the value of this design for
political science applications. Conjoint
experiments present respondents with a
choice among set of profiles composed of
multiple randomly assigned attributes.
This approach allows researchers to
estimate the effect of each individual
component on the probability that the
respondent will choose a profile. This
software tool is designed as a companion
to Hainmueller,
Hopkins and Yamamoto (2013),
providing a graphical user interface for
generating conjoint experiments.